Data Analysis for Genomics

http://genomicsclass.github.io/book/

HarvardX: PH525x Data Analysis for Genomics

Data Analysis for Genomics

The repository of the R markdown files (.Rmd) for the labs shown here is:

http://github.com/genomicsclass/labs

Resources

• Online resources and books
• Finding more help for data analysis

Introduction (week 1)

• Introduction
• Exploratory Data Analysis
• Installing Bioconductor and finding help
• R refresher
• Robust summaries

Microarray and NGS basics (week 2)

• Installing packages from Github
• Reading microarray data
• Downloading data from GEO using GEOquery
• EDA plots for microarray
• Basic Bioconductor infrastructure
• EDA plots for next generation sequencing

Statistical inference and linear modeling (week 3)

• Inference
• Expressing design formula in R
• Linear models
• Basic inference for microarray
• Rank tests
• Monte Carlo methods

Background, modeling and normalization (week 4)

• Background
• Modeling
• Normalization

Distance and prediction (week 5)

• Distance lecture
• Distance and clustering lab
• Dimension reduction and heatmaps
• Prediction lecture
• Cross-validation

Batch effect (week 6)

• Confounding
• SVA, ComBat, PCA and SVD
• Running PCA and SVD in R

Advanced differential expression (week 7)

• Hierarchical modeling and using limma
• Mapping features to genes
• Gene set analysis lecture
• Gene set testing in R
• Multiple testing

Advanced workflows (week 8)

• Visualizing NGS data
• Counting NGS reads in features
• Methylation
• Reading 450K idat files with the minfi package
• Interactive visualization of DNA methylation data analysis
• ChIP-seq
• RNA-seq
• Genome variation

Footnotes for all lectures

• Footnotes